Evaluating the Benefit of a Urogynecologic Telehealth Consultation after Obstetric Anal Sphincter Injury
Bibliographic record
Abstract
INTRODUCTION: Obstetric anal sphincter injuries (OASI) are associated with significant risk of complications, including pain, infection, and long-term pelvic floor dysfunction. The primary aim of this study was to evaluate the utility and acceptability of a postpartum telehealth consultation focused on pelvic floor health for patients after OASI. METHODS: This prospective study used a pre-post design comparing standard postpartum care versus standard postpartum care plus a telehealth urogynecology consultation focused on pelvic floor recovery. The primary outcome was symptom burden as measured by the Pelvic Floor Distress Inventory (PFDI-20) score 16-weeks postpartum. Patient experience was evaluated using the QQ10 and the Patient Enablement Instrument. T-tests and chi-squared tests were used to compare groups. RESULTS: A total of 119 participants completed study activities (control group n = 62, intervention group n = 57). There was no significant difference between the two groups in PFDI-20 scores (55.6 versus 46.6, p = 0.23). The individual items most likely to be endorsed among all participants were related to flatal incontinence (52.1%) and fecal urgency (49.6%). For the subset analysis of 35 patients with severe OASI (3C or fourth-degree tears), those who had a telehealth consultation had lower PFDI-20 scores (56.6 versus 34.7; p = 0.04). QQ10 estimated a value score of 79/100 and a burden score of 18/100 for the telehealth consultation. CONCLUSIONS: A postpartum telehealth consultation focused on pelvic floor health may benefit patients with severe OASI who reported reduced symptom burden. Participants rated a telehealth consultation as high value and low burden for this condition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".